Abstract It is feared that the increasing population of vehicles in the world and the depletion of fossil-based fuel reserves could render transportation and other activities that rely on fossil fuels unsustainable in the long term. Concerns over environmental pollution issues, the high cost of fossil-based fuels and the increasing demand for fossil fuels has led to the search for environmentally friendly, cheaper and efficient fuels. In the search for these alternatives, liquefied petroleum gas LPG has been identified as one of the viable alternatives that could be used in place of gasoline in spark-ignition engines. The objective of the study was to present the modeling and multi-objective optimization of brake mean effective pressure and hydrocarbon emissions for a spark-ignition engine retrofitted to run on LPG. The use of a one-dimensional 1D GT-Power™ model, together with Group Method of Data Handling GMDH neural networks, has been presented. The multi-objective optimization was implemented in MATLAB® using the non-dominated sorting genetic algorithm NSGA-II. The modeling process generally achieved low mean squared errors 0.0000032 in the case of the hydrocarbon emissions model for the models developed and was attributed to the collection of a larger training sample data using the 1D engine model. The multi-objective optimization and subsequent decisions for optimal performance have also been presented. View Full-Text